CSE 705: Deep Learning on Graphs (Spring 2022)

Instructor: A. Erdem Sariyuce (erdem AT buffalo.edu)
Class hours: Wed 10:00-12:40, Greiner 134C/135C
Office hours: Wed 3:30-5:30, Online over Zoom (link)

Course Description

Graphs are everywhere. Their scale, rate of change, and the irregular nature pose many new challenges. Deep learning has been shown to be successful in a number of domains, ranging from images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics. This seminar course covers recent papers in the last few years about deep learning on graphs. We will consider graph embeddings, knowledge graphs, graph kernels, graph neural networks, graph convolutional networks, graph adversarial methods. Students will learn the literature on deep learning on graphs, understand the state-of-the-art algorithms on various problems, and be familiar with the recent trends.


It is assumed that students have a solid background on discrete mathematics and algorithms. Basic research skills like paper reading, critical thinking, problem solving, report writing, communication, and presentation are important as well.

Grading Policy

1, 2, or 3 credits
  • Paper presentation: 20 pts
  • Piazza questions: 7.5 pts (one question)
  • Class participation: 10 pts (in total)
  • Attendance: 3 pts (one class)

The final grade is S/U and 70 pts score is needed for an S.

Paper presentation & questions

Students will do the presentations. Tentative schedule is below. A presentation is expected to be 45 mins long. Each week, all the students will read the paper of the week before class and half of the students (I'll tell which half) will ask a unique question on Piazza (except the presenter) to facilitate a class discussion. Questions should be open-ended and provide ground for class discussions, i.e., 'can you explain alg 1?' is not that kind of question. Questions should be posted to Piazza by Monday night, 11.59 pm EST.

Piazza page